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Orderly Subspace Clustering.

Wang, J., Suzuki, A., Linchuan, X., Tian, F., Liang, Y. and Yamanishi, K., 2019. Orderly Subspace Clustering. In: AAAI Conference on Artificial Intelligence, 27 January-1 February 2019, Hawaii, USA.

Full text available as:

Final-OSC.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.



Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure. Meanwhile, a reliable and easy-to-obtain supervision is desirable for practical learning. To meet these two objectives, in this paper we make the first attempt towards utilizing the orderly relationship, such as the data a is closer to b than to c, as a novel supervision. We propose an orderly subspace clustering approach with a novel regularization term. OSC enforces the learned representations to simultaneously capture the intrinsic subspace structure and reveal orderly structure that is faithful to true data relationship. Experimental results with several benchmarks have demonstrated that aside from more accurate clustering against state-of-the-arts, OSC interprets orderly data structure which is beyond what current approaches can offer.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:31937
Deposited By: Symplectic RT2
Deposited On:04 Mar 2019 11:55
Last Modified:14 Mar 2022 14:15


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